Evaluation of Home and Community Based Waiver Program Survey Methodology

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1 Contract No.: TLG MPR Reference No.: Evaluation of Home and Community Based Waiver Program Survey Methodology September 23, 2005 Larry P. Snell Zhanyun Zhao Cathy Lu Frank Potter Anne B. Ciemnecki Submitted to: The Lewin Group, Inc. Suite 800, 3130 Fairview Park Dr. Falls Church, VA Project Officer: Lisa Marie B. Alecxih Submitted by: Mathematica Policy Research, Inc. P.O. Box 2393 Princeton, NJ Telephone: (609) Facsimile: (609) Project Director: Anne B. Ciemnecki

2 CONTENTS Chapter Page I OVERVIEW...1 II SAMPLE SELECTION AND ALLOCATION...4 A. SAMPLING FRAMES: DESCRIPTION AND PROCESSING PROCEDURES...5 B. SAMPLING DESIGN...7 C. ALLOCATING SAMPLE TO STATES...10 III DATA COLLECTION PROCEDURES...12 A. INTERVIEWER TRAINING...12 B. DATA COLLECTION SCHEDULE...13 C. RESPONDENT NOTIFICATION...13 D. USE OF PROXY RESPONDENTS...14 E. MONITORING INTERVIEWER PERFORMANCE...15 F. OBTAINING CONTACT INFORMATION FOR SAMPLE MEMBERS...15 G. SURVEY ELIGIBILITY...17 IV WEIGHTING AND NON-RESPONSE ADJUSTMENTS...18 A. PREPARATION FOR WEIGHTING...18 B. LOCATION MODEL...20 C. RESPONSE MODEL...24 APPENDIX A APPENDIX B APPENDIX C ADVANCE LETTERS FINAL SURVEY STATUS REPORT QUESTIONNAIRE ii

3 I. OVERVIEW The Lewin Group, Inc (Lewin) is conducting an evaluation of the Medicaid Home and Community Based Service Waivers (HCBS) for the United States Department of Health and Human Services Centers for Medicare & Medicaid Services (CMS). Collaborating on the project as subcontractors are: Mathematica Policy Research (MPR), Research Triangle Institute (RTI), the University of Minnesota, the MEDSTAT Group (MEDSTAT), and Human Services Research Institute (HSRI). CMS is responsible for the federal administration of the Medicaid program (it also administers the Medicare program). Medicaid is a joint federal and state program that helps pay medical costs for some people with low incomes and limited resources. Medicaid HCBS waiver programs provide states with greater flexibility to serve individuals with substantial long-term care needs at home, or in the community, rather than in institutions. The number of, and expenditures for, these waivers continue to grow dramatically despite little research documenting the effects of services on cost, quality of care, or quality of life of both recipients and their families. Therefore, CMS wished to evaluate selected programs to assess their effects on quality of care, satisfaction with services, general health and functional status, quality of life, care management and cost of providing services. CMS also hoped to identify features of programs that are associated with favorable outcomes. The growing availability of home and communitybased care is transforming the nature of formal (paid) long-term care services in the United States. These newly available services permit individuals to receive long-term care in their homes or communities rather than in nursing homes. In the past, a lack of alternatives forced individuals in need of long-term care to choose between relying almost exclusively on their family and friends, or being institutionalized. Now, many individuals receive formal services 1

4 that enable them to live alone at home, receive assistance with daily activities that support and relieve their informal (unpaid) caregivers, move to a more home-like facility, such as an adult foster home. Home and community-based services, such as skilled nursing in the home and help with bathing, have become a more important part of the package of services offered by Medicaid. The provision of home and community-based support services is thought to prevent or delay institutionalization. This report summarizes the methods MPR used to conduct the survey for the evaluation, the processes used to select samples for the Home and Community Based Services and the processes used to adjust the sampling weights to account for non-response. Data were collected by telephone and through in-person interviews during a fifty eight week field period between May 5, 2003 to June 14, 2004, in six participating states. Three states, Alabama, Kentucky, and Maryland, had developing programs; three states, Michigan, Washington and Wisconsin, had developed programs. The survey samples were drawn from the state Medicaid files. In Alabama, Michigan, and Washington, the sample consisted of waiver program participants during November In Maryland, the sample consisted of participants during December In Kentucky, the sample members could have participated at any time during In Wisconsin, sample members participated during December In Wisconsin, records for HCBS waiver maintained at the counties and are submitted to the state only once a year in approximately August. When negotiating sample acquisition with the state, we chose to obtain what was available rather than wait for the 2001 sample. Although we requested sample from the November 2001 timeframe, several of the states could not provide samples until the summer of

5 Telephone interviews used Computer Assisted Telephone Interviewing (CATI) technology. In total 5,405 cases were released to interviewing. Substantial effort was devoted to updating contact information on the sample frames. To obtain telephone numbers and updated addresses, MPR used Marketing Systems Group (MSG) who sent the file to Experian for NCOA (National Change Of Address) updates, then MSG used their database to append new phone numbers or verify existing telephone numbers. In total, just eight percent could not be located for an interview or to confirm eligibility. To be eligible to participate in the survey, sample members could not be deceased or living in a convalescent or nursing home or institution for more than 30 days. They had to be receiving home support services and still residing in the state where they were sampled or in Wisconsin, still residing in the county where they were sampled. Because of the age of the sampling frames, nearly one-third of sampled respondents were not interviewed most (59 percent) were deceased. Many (17 percent) were institutionalized. A total of 2, minute interviews were completed, of which 2,458 (95 percent) were completed by phone using CATI. The remainder were completed in person. Cooperation rates among respondents were high, 92 percent of those who were located and eligible agreed to be interviewed. Four of the interviews turned out to be duplicates and were dropped from the analysis. In the end, the analysis is based on 2,597 program participants. 3

6 II. SAMPLE SELECTION AND ALLOCATION This chapter describes the sampling design. The target population for the HCBS study consisted of all adult Medicaid home and community-based services waiver and state plan personal care option recipients, aged 18 and older in six states. Persons who were mentally retarded (MR) or developmentally disabled (DD) were excluded from this population by the states based on HCBS waiver type. A simple random sample was selected in each of the six states. Across the six states, the lists provided by the individual states contained 87,526 persons and 204 were determined ineligible. 1 From the final sampling frame of 87,322 persons, an initial sample of 14,995 persons was selected and the final fielded sample contained 5,405 persons. 2 We located 4,973 persons (92 percent) of the sample and 4,364 persons (88 percent) of the located persons responded or were ineligible to respond. The overall response rate was 80.7 percent. The sample was selected using a sequential random sampling procedure and initial weights were computed from the inverse of the selection probability. Prior to sample selection, we investigated the use of geographical clustering of the sample because of the high cost of in-person interviewing of those who could not be interviewed by telephone. Based on the review of the profile of the sampling frame information, we 1 Ineligible persons included persons living outside the study state as well as persons who were mentally retarded or had a developmental disability. 2 The initial sample was randomly partitioned into subsamples called waves. The number of waves used in any one state varied according to response and eligibility among sample members. 4

7 recommended against the clustering of all samples because the cost saving from clustering would be too small to justify the loss of sampling precision. We describe the processing procedures of the sampling frame first, followed by procedures of sample allocation. The procedures for calculating the weights and nonresponse adjustment are described in Chapter IV. A. SAMPLING FRAMES: DESCRIPTION AND PROCESSING PROCEDURES By September 2002, we received the lists of the HCBS recipients from each individual state government. For each state list, we conducted a review of the data records and the data elements. In this review, we identified potentially ineligible cases with out-of-state addresses, persons under the age of 18, and given the information available, the appropriate waiver type classification. For the under age 18 cases, we found few of these and they were often associated with future birthdates or were sufficiently close to age 18 at the time. It was decided not to delete them from the sampling frame nor the samples with the understanding that they would be identified during the interviewing process if in fact they were under age. We deleted all out-ofstate cases prior to sample selection. This was a very complex sample with multiple agencies in multiple states, each submitting the data differently. A four of the states nuances in either the files or the processes to obtain the file which are described below: The Maryland file contained participants with mental retardation and developmental disabilities as well as the participants who were aged and disabled. We excluded the MR/DD sample from the frame prior to selecting the sample. The Michigan sample frame contained two files. One, of Michigan Choice participants, had 8,643 cases. The other, from the Michigan home health program, had 28,135 cases. The Michigan Choice file was inadvertently not used when drawing the sample. This means that the analysis had fewer waiver participants and more state plan personal care participants in the developed sample than planned. Because the analyses controls for whether an individual used these two types of 5

8 services, the omission of the Michigan HCBS waiver sample does not alter the analyses. This omission should also have little impact on the interpretation of the analyses, again because the variable will be used as a control for the regression analyses. In Wisconsin, five Family Care counties, Milwaukee, La Crosse, Fond du Lac, Portage, and Richland, all which had a capitated Medicaid long-term care demonstration in progress, were ineligible for the survey. Because the Washington State IRB required passive consent, the state sent MPR a file of 28,557 participants that had sample stratification variables such as age and gender but not personally identifying information. MPR selected a sample of 4,500 program participants and returned that file to the state. The state would only mail 3,000 letters, so MPR trimmed the sample to 3,000. The state linked the file to contact information and mailed 2,962 letters requesting permission to provide MPR with contact information for the survey. Program participants were to return a prepaid post card within two weeks if they did not want to participate in the survey. Of the 2,962 letters mailed, 510 were returned as non deliverable and 738 returned post cards indicating that they did not want to participate. Washington then provided contact information for the remaining 1,714 cases from which MPR selected a survey sample of 1,246. Washington State would not release social security numbers for program participants either. In Washington, only, we had to ask respondents for their social security numbers. Of the 601 survey respondents, 370 provided social security numbers. All but two could be matched to claims data. In addition to a review of the eligibility requirements, we also classified the addresses into the following three address classes Common street addresses (for which locating a phone number is typically easier than for other types of addresses) 2. P.O. Box, Rural Route, General delivery or other non-standard street addresses (for which phone number searches tend to be unsuccessful) 3. Duplicate/common street addresses. To form this classification, we conducted an address parsing coupled with a phonic-based address duplicate detection procedure based on the Double-Take 4 software package. Any cases 3 Address information was not provided by Washington to prepare these address classes. 4 Double-Take, Version 2.005, 32-bit, Distributed by Peoplesmith Software, 50 Cole Parkway, Suite 34, Scituate, MA

9 identified as address duplicates were placed into one address class (class 3) and the remaining cases were placed into either address class 1 or 2 based on whether the address was a standard street address, or a Post Office Box or other Box type address that would not identify the location of the residence. The benefit of separating out the duplicate addresses in this fashion was to allow the interviewer to contact a facility administrator/apartment manager to obtain contact information for multiple sample members at one time. We also conducted a visual review of the cases with common facility names to determine if the frame potentially included institutionalized persons, which would be determined to be ineligible during the interview. Table 1 gives a profile of each sampling frame. B. SAMPLING DESIGN Originally, the proposed sampling method was simple random sampling. Because high costs would be associated with in-person follow-up, we considered, but in the end rejected, the use of a geographically clustered sample design. To investigate whether to cluster the samples we determined the geographical dispersion of the cases in the frames by computing the person s distance from the center of the nearest large metropolitan area. To determine the distance, we obtained the longitude and latitude for each person s ZIP code and that for each of the centroids of the largest MSA counties (typically 4-15 in each state). We then used a trigonometric formula to convert the differences between the person s longitude and latitude points and that of the MSA counties to mileage-based distances. The average distances and frequencies by distance ranges are presented in Table 2. 7

10 TABLE 1 SUMMARY OF SAMPLING FRAME, PERSON COUNT AND CHARACTERISTICS AL KY MD MI WA WI Records received 5,728 12,897 3,343 28,135 28,562 8,861 Records eliminated a Final Frame 5,716 12,888 3,340 28,083 28,557 8,738 Age 0-17 years years , years , years ,055 1, years 467 1, ,774 3, years 682 1, ,635 3,828 1, years 869 2, ,471 5,709 1, years 1,384 3,143 1,000 3,704 7,219 2, years 1,139 1, ,513 4,844 1, years , Address type class 1. Standard street 4,134 5,033 2,069 22,048 7, P.O. Box address 777 1, , Duplicate type 805 6,449 1,190 4, a The cases eliminated in WI were nursing home residents who were no longer eligible for HCBS services. TABLE 2 DISTANCE OF PERSONS TO NEAREST METROPOLITAN AREA AL KY MD MI WA WI Average Distance Frequency 0-25 miles 2,706 3,198 2,908 17,255 19,390 4, miles 1,963 2, ,388 3,341 1, miles 1,045 6, ,204 4,934 1, miles , miles 2 4 1, ,242 Unknown Total Persons 5,716 12,888 3,340 28,083 28,557 8,738 8

11 The results in Table 2 show that the average distances to the nearest city for considering the use of geographical clustering of the sample were sufficiently small for all but Wisconsin and Kentucky. For Wisconsin, because the majority (65 percent) of the cases were less than 50 miles from a major city, we decided to implement the original proposed simple random sampling methodology. On the other hand, Kentucky had a wide spread of cases and required more careful consideration; however, we decided that the best design for Kentucky was also the originally proposed methodology. Our rationale for not geographically clustering the Kentucky sample stems from the unique characteristics of the population under study in this state. Specifically, we found that the population was small and the population to be highly concentrated in a few areas and very dispersed throughout the rest of the state. As an example, when we examined the population counts at the 5-digit ZIP code level, we found 70 percent of them to have less than 10 persons each, with a handful containing between cases. We also found similar results at the county level with one county containing 10 percent of all the cases, and 20 percent of the counties having less than 20 cases each (many of these contain only 1 or 2 persons). This presented a problem for clustering the sample in that if we tried to keep the geographical land area of the interviewer areas/clusters to be relatively small to save travel time once in the area, we ended up having to select many areas to obtain a sufficient number of persons to meet the sampling and interviewing requirements. Because so many areas would be selected, they ended up basically covering the entire state, thus defeating the purpose for the clustering. In addition, because the in-person interviewing workload in this state would be small (less than 400 interviews), a sufficient workload could not exist in any one area to support hiring local staff. Based on these considerations, we felt that clustering the sample would not be sufficiently cost 9

12 effective to warrant the reduction in sampling precision that results from the clustering process. Therefore, we used a simple random sample in each state. C. ALLOCATING SAMPLE TO STATES The proposed sampling and interviewing process seeks to allocate the interviews proportionally among the developing and developed states (See Table 3). For each state, we selected a relatively large initial sample and divided the sample into random replicates or waves. We planned to release the waves in phases to ensure that each case receives the same level of effort during the interviewing process. To manage the waves, we made an initial large release to meet the interviewing requirements at an optimistic 90 percent response rate. Based on the findings from that release, we released additional waves as needed to obtain the desired number of completed interviews. TABLE 3 INITIAL SAMPLE ALLOCATION AND FINAL SAMPLE BY STATE AND SAMPLE WAVE INFORMATION Developing Programs Developed Programs Developing Programs Developed Programs AL KY MD MI WA WI Total Population 5,716 12,888 3,340 28,083 28,557 8,738 87,322 21,944 65,378 Target Allocation 625 1, ,031 1, ,800 2,400 2,400 Initial Sample 2,395 3,000 1,200 3,000 4, ,995 6,595 8,400 Number of Waves Average Cases Per Wave Final Sample 683 1, ,135 1, ,405 2,664 2,741 To select each sample, we utilized a sequential random sample selection procedure that sorts the beneficiaries in each the sampling stratum in a serpentine fashion based on a set of specified characteristics. This process, outlined by Chromy (1979), 5 imposes implicit stratification beyond 5 Chromy, James R. (1979) Sequential Sample Selection Methods, In Proceedings of the American Statistical Association Survey Research Section, pages

13 the primary strata to ensure the sample is balanced on the implicit stratification variables. In this study, each state basically serves as the primary sampling strata. Within each state, we sorted the records using a serpentine methodology based on the age category (as presented in Table 1) and 3-digit ZIP code to ensure approximate proportional representation by these dimensions within each state. The final sample included 5,405 persons. The distribution of the actual frame count, target allocation of the sample and the final sample by state is shown in Table 3. 11

14 III. DATA COLLECTION PROCEDURES The telephone data collection was conducted at MPR s Plainsboro, New Jersey survey operations center. In total, 2,601 interviews were completed; 2,597 were usable for the analysis. Of the 2,601, 1,714 interviews were completed by telephone with beneficiaries and 744 interviews were completed by telephone with a proxy for the beneficiary. These interviews were conducted by telephone using MPR s Computer Assisted Telephone Interviewing system (CATI). Of the remaining 143 interviews, 142 interviews were completed in person with the beneficiary and one interview was completed in person with a proxy for the beneficiary. The inperson interviews were conducted using a hardcopy instrument. Trained telephone interviewers traveled to the six states to conduct the in-person interviews. The average interview length was 36 minutes. A. INTERVIEWER TRAINING MPR trained 40 telephone interviewers to administer the survey instrument. All but six of the interviewers trained had prior experience conducting telephone interviews. Study-specific training took twelve hours. Trainers explained the background and purpose of the study, reviewed the questionnaire, provided instructions for asking each question, and discussed methods for contacting respondents and gaining cooperation. In addition, we trained the interviewers on the challenges of interviewing people with disabilities. Interviewers had ample time for role playing, practice interviewing, and administrative procedures. After the main session, interviewers finished their training by completing practice interviews with a supervisor. 12

15 B. DATA COLLECTION SCHEDULE Interviewing began in Alabama, Kentucky, Maryland, Michigan and Wisconsin on May 5, 2003 and began in Washington on September 29, A total of 2,705 cases were in the first sample release. On March 15, 2004 an additional 2,700 cases were released to interviewing across the six states. Interviewing ended on June 14, Table 4 shows the data collection progress by state and month. TABLE 4 COMPLETED INTERVIEWS BY MONTH Month, Year Alabama Kentucky Maryland Michigan Washington Wisconsin Monthly Total Cum Totals May, June, July, August, September, October, November, December, ,108 January, ,199 February, ,251 March, ,599 April, ,117 May, ,475 June, ,601 Total ,601 C. RESPONDENT NOTIFICATION In all states except Washington, sampled beneficiaries were notified by mail one week before an initial call was made to reassure them about the survey s authenticity and purpose. The advance letter was on CMS letterhead and explained the purpose of the study, confidentiality of responses, and voluntary participation (see Appendix A). The letter encouraged 13

16 respondents to call MPR s toll-free number for further information and to participate in the study. An Institutional Review Board in Washington required passive consent prior to survey participation and would not share contact information with MPR until Medicaid recipients who did not wish to participate in the study had an opportunity to opt out. The Washington State Department of Social and Health Services, Aging and Disability Services Administration mailed an advance letter that explained the purpose of the study, confidentiality of responses, and voluntary participation to their program participants. As stated earlier, the mailing contained a postcard for participants to send back to the state if they did not wish to have their contact information shared with MPR. Those who did not return postcards and whose advance letters were not returned as undeliverable become eligible for the survey sample. Those selected received the same advance letter as program participants in other states. D. USE OF PROXY RESPONDENTS Despite their age and disabling conditions, 71 percent of the respondents were able to answer survey questions on their own. Twenty eight percent of the completed interviews were conducted by a proxy respondent. A proxy is defined as a person who completed an interview on behalf of the sample member. The proxy was recruited when interviewers learned that sample members were unable to complete the interview themselves due to a physical or mental condition such as hearing impairment, or dementia. Interviewers also recruited proxies to complete the interview for sample members having language barriers. Eligible proxies included individuals familiar with the health care experiences of the sample member. They were often the spouses, children, or other relatives and friends of the sample member. If family members or friends were not available, a volunteer or unpaid personal care or home helper may have been a proxy respondent. As a last resort, a paid personal care or home helper who was in frequent (at 14

17 least weekly) contact with the sample member may have been a proxy respondent. The single most common reason for using a proxy was because the sampled beneficiary was not cognitively or mentally able to complete the interview. Thirty percent of sample members under age 65 needed proxy respondent while 27 percent of sample members age 65 or older relied on proxy respondents. Elderly sample members were more likely than younger respondents to require inperson rather than telephone interviews (7 versus 3 percent). E. MONITORING INTERVIEWER PERFORMANCE Both qualitative and quantitative indicators of interviewer performance were used to monitor data quality. Quantitative indicators, such as productivity and refusal rates were assessed from reports generated by the CATI system (see Appendix A). During the first week of the project, at least one completed interview was monitored for each telephone interviewer using MPR s central monitoring system. The system enables the supervisor to listen to interviews without the interviewer or the respondent being aware of it. The system also allows the supervisor to view the interviewer s CATI screen while the interview is in progress. Overall, approximately 7 percent of all interviews were monitored. For each monitored interview, the supervisor completed an on-line evaluation identifying specific errors. At the completion of the monitoring session, the supervisor reviewed any errors with the interviewers and made suggestions for improvement. F. OBTAINING CONTACT INFORMATION FOR SAMPLE MEMBERS The state files from Alabama, Kentucky, Maryland, Michigan and Wisconsin contained a telephone number for 42 percent of the cases. To obtain telephone numbers and updated addresses for the remaining cases, MPR used Marketing Systems Group (MSG) who sent the file to Experian for NCOA (National Change Of Address) updates, then MSG used their database to 15

18 append new phone numbers or verify existing telephone numbers. This search yielded telephone numbers for 65 percent of the cases, of which 48 percent did not have a prior telephone number. The state file from Washington contained a phone number for 97 percent of the cases. MPR sent this file to MSG to append new phone numbers or verify existing telephone numbers. This search yielded matched telephone numbers for 56 percent of the cases. Telephone numbers that were not verified were still used when possible. Therefore, at the start of data collection 80 percent of the cases had telephone numbers. In addition to the 20 percent of cases for which a telephone number was not initially located, 41 percent of the sample had an incorrect telephone number. MPR s locating department was able to locate a telephone number for 63 percent of these cases and determined that twenty four percent of these sampled beneficiaries were deceased. The following resources were used to locate sample beneficiaries: Directory Assistance. The locating specialist asked the operator for the sampled respondent and others in the area with the same or similar last names. During the data collection period new on-line services allowed the locating specialist to perform their own directory assistance searches. Advance Letter mailed ADDRESS SERVICE REQUESTED. The US Postal Service will return a letter with updated address information when it is available. On-line database of addresses. On-line databases were used to verify or update address information for sample beneficiaries. These databases were also used to look up cases by address, also called reverse address look-ups. Reverse address look-ups sometimes yielded a telephone number that was listed to someone other than the sample beneficiary. Neighbors. Reverse look-ups were used to obtain the names and telephone numbers of neighbors. Neighbors provided useful locating leads and took messages. Our general approach to locating was to use the least expensive, automated sources first and progress to the more expensive locating for cases that were not found. 16

19 G. SURVEY ELIGIBILITY Five thousand four hundred and five (5,405) cases were released for interviewing. Thirty three percent (1,784 cases) were ineligible for survey participation. There were four reasons for ineligibility: the sample member was deceased (1,101 cases), the sample member was in a convalescent or nursing home or institution for more than 30 days (315 cases), the sample member either never received or no longer received home support services (343 cases) and the sample member no longer resided in the state where they were sampled or for Wisconsin, the county where they were sampled (25 cases). We did not conduct interviews with these cases. 17

20 IV. WEIGHTING AND NON-RESPONSE ADJUSTMENTS We computed initial weights based on the selection probability and adjusted these weights for the proportion of the initial sample that was used in the survey. We then computed a final location and response-adjusted weight by running two consecutive logistic propensity models for each of the six states. A Chi-squared Automatic Interaction-Detection (CHAID) algorithm and logistic regression were used to identify factors associated with nonresponse and to develop the logistic models. The R-squares for the logistic models range from 0.03 to 0.09, and the Hosmer- Lemeshow goodness-of-fit statistics range from 0.42 to 0.99 for the 12 models. The design effects of unequal weighting are very close to 1 for all of the models. The sampling weights are computed from the probability of selection with adjustments for the proportion of the initial sample released and for nonresponse. The non-response adjustment was done separately within each individual state. The non-response method used was logistic response propensity regression modeling in two stages: first for the ability to locate the person and then for response among the located person. A. PREPARATION FOR WEIGHTING Because of the difference between the number of total waves and the number of released waves, the initial weight was adjusted to account for the number of waves used and is: (1) #of total waves Wgt _ input = Wgt _ sampling #of released waves Here, Wgt _ sampling is the initial sampling weight when the sample was selected in October The release-adjusted initial weights were then post-stratified so that the sum of 18

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